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<a href="_contrastive_divergence_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">/*!</span></div>
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment"> * </span></div>
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment"> *</span></div>
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment"> * \brief       -</span></div>
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment"> *</span></div>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span><span class="comment"> * \author      -</span></div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="comment"> * \date        -</span></div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="comment"> *</span></div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="comment"> *</span></div>
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="comment"> * \par Copyright 1995-2017 Shark Development Team</span></div>
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="comment"> * </span></div>
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="comment"> * &lt;BR&gt;&lt;HR&gt;</span></div>
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span><span class="comment"> * This file is part of Shark.</span></div>
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span><span class="comment"> * &lt;https://shark-ml.github.io/Shark/&gt;</span></div>
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span><span class="comment"> * </span></div>
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span><span class="comment"> * Shark is free software: you can redistribute it and/or modify</span></div>
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span><span class="comment"> * it under the terms of the GNU Lesser General Public License as published </span></div>
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span><span class="comment"> * by the Free Software Foundation, either version 3 of the License, or</span></div>
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<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span><span class="comment"> * GNU Lesser General Public License for more details.</span></div>
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span><span class="comment"> * </span></div>
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span><span class="comment"> * You should have received a copy of the GNU Lesser General Public License</span></div>
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span><span class="comment"> * along with Shark.  If not, see &lt;http://www.gnu.org/licenses/&gt;.</span></div>
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span><span class="comment"> *</span></div>
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span><span class="comment"> */</span></div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span><span class="preprocessor">#ifndef SHARK_UNSUPERVISED_RBM_GRADIENTAPPROXIMATIONS_CONTRASTIVEDIVERGENCE_H</span></div>
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span><span class="preprocessor">#define SHARK_UNSUPERVISED_RBM_GRADIENTAPPROXIMATIONS_CONTRASTIVEDIVERGENCE_H</span></div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span> </div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span><span class="preprocessor">#include &lt;<a class="code" href="_abstract_objective_function_8h.html" title="AbstractObjectiveFunction.">shark/ObjectiveFunctions/AbstractObjectiveFunction.h</a>&gt;</span></div>
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span><span class="preprocessor">#include &lt;<a class="code" href="_energy_8h.html">shark/Unsupervised/RBM/Energy.h</a>&gt;</span></div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span> </div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span><span class="keyword">namespace </span><a class="code hl_namespace" href="namespaceshark.html" title="AbstractMultiObjectiveOptimizer.">shark</a>{</div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span><span class="comment"></span> </div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span><span class="comment">/// \brief Implements k-step Contrastive Divergence described by Hinton et al. (2006).</span></div>
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span><span class="comment">///</span></div>
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span><span class="comment">/// k-step Contrastive Divergence approximates the gradient by initializing a Gibbs</span></div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span><span class="comment">/// chain with a training example and run it for k steps. </span></div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span><span class="comment">/// The sample gained after k steps than samples is than used to approximate the mean of the RBM distribution in the gradient.</span></div>
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span><span class="comment"></span><span class="keyword">template</span>&lt;<span class="keyword">class</span> Operator&gt;    </div>
<div class="foldopen" id="foldopen00044" data-start="{" data-end="};">
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno"><a class="line" href="classshark_1_1_contrastive_divergence.html">   44</a></span><span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_contrastive_divergence.html" title="Implements k-step Contrastive Divergence described by Hinton et al. (2006).">ContrastiveDivergence</a>: <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_abstract_objective_function.html">SingleObjectiveFunction</a>{</div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span><span class="keyword">public</span>:</div>
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno"><a class="line" href="classshark_1_1_contrastive_divergence.html#a25fb98f539ea0bf360835711d7608402">   46</a></span>    <span class="keyword">typedef</span> <span class="keyword">typename</span> Operator::RBM <a class="code hl_typedef" href="classshark_1_1_contrastive_divergence.html#a25fb98f539ea0bf360835711d7608402">RBM</a>;</div>
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span>    <span class="comment"></span></div>
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span><span class="comment">    /// \brief The constructor </span></div>
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span><span class="comment">    ///@param rbm pointer to the RBM which shell be trained </span></div>
<div class="foldopen" id="foldopen00051" data-start="{" data-end="}">
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno"><a class="line" href="classshark_1_1_contrastive_divergence.html#a7b5c3fb150a0a986990ef1a5ce80c870">   51</a></span><span class="comment"></span>    <a class="code hl_function" href="classshark_1_1_contrastive_divergence.html#a7b5c3fb150a0a986990ef1a5ce80c870" title="The constructor.">ContrastiveDivergence</a>(<a class="code hl_typedef" href="classshark_1_1_contrastive_divergence.html#a25fb98f539ea0bf360835711d7608402">RBM</a>* rbm)</div>
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span>    : mpe_rbm(rbm),m_operator(rbm)</div>
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span>    , m_k(1), m_numBatches(0),m_regularizer(0){</div>
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span>        <a class="code hl_define" href="_exception_8h.html#a73abb5049a0168d72a48e72dda41708b">SHARK_ASSERT</a>(rbm != NULL);</div>
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span> </div>
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span>        <a class="code hl_variable" href="classshark_1_1_abstract_objective_function.html#ad8888c58fd3f98e73013afb5dd4b2af1">m_features</a>.<a class="code hl_function" href="classshark_1_1_typed_flags.html#a68f0c572adf112b680ef11531aa9ffb8">reset</a>(<a class="code hl_enumvalue" href="classshark_1_1_abstract_objective_function.html#aadafeb6dfb5b649f321e7b81ac8aad1aad3475b458576c8760f28d8d81f4eda86" title="The function can be evaluated and evalDerivative returns a meaningless value (for example std::numeri...">HAS_VALUE</a>);</div>
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span>        <a class="code hl_variable" href="classshark_1_1_abstract_objective_function.html#ad8888c58fd3f98e73013afb5dd4b2af1">m_features</a> |= <a class="code hl_enumvalue" href="classshark_1_1_abstract_objective_function.html#aadafeb6dfb5b649f321e7b81ac8aad1aa0bc7673a369df5f86ddd6ba6735f4971" title="The method evalDerivative is implemented for the first derivative and returns a sensible value.">HAS_FIRST_DERIVATIVE</a>;</div>
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span>        <a class="code hl_variable" href="classshark_1_1_abstract_objective_function.html#ad8888c58fd3f98e73013afb5dd4b2af1">m_features</a> |= <a class="code hl_enumvalue" href="classshark_1_1_abstract_objective_function.html#aadafeb6dfb5b649f321e7b81ac8aad1aab9262b57bb302f04b2561666a9068446" title="The function can propose a sensible starting point to search algorithms.">CAN_PROPOSE_STARTING_POINT</a>;</div>
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span>    };</div>
</div>
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span><span class="comment"></span> </div>
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span><span class="comment">    /// \brief From INameable: return the class name.</span></div>
<div class="foldopen" id="foldopen00062" data-start="{" data-end="}">
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno"><a class="line" href="classshark_1_1_contrastive_divergence.html#ae60eb82e6c409d2f2a0f9fb8310a81f1">   62</a></span><span class="comment"></span>    std::string <a class="code hl_function" href="classshark_1_1_contrastive_divergence.html#ae60eb82e6c409d2f2a0f9fb8310a81f1" title="From INameable: return the class name.">name</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> <span class="stringliteral">&quot;ContrastiveDivergence&quot;</span>; }</div>
</div>
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span><span class="comment"></span> </div>
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span><span class="comment">    /// \brief Sets the training batch.</span></div>
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span><span class="comment">    /// @param data the batch of training data</span></div>
<div class="foldopen" id="foldopen00068" data-start="{" data-end="}">
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno"><a class="line" href="classshark_1_1_contrastive_divergence.html#a2471eeb8e6d309b0fa983fdbc5879d9b">   68</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_contrastive_divergence.html#a2471eeb8e6d309b0fa983fdbc5879d9b" title="Sets the training batch.">setData</a>(<a class="code hl_class" href="classshark_1_1_unlabeled_data.html" title="Data set for unsupervised learning.">UnlabeledData&lt;RealVector&gt;</a> <span class="keyword">const</span>&amp; data){</div>
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span>        m_data = data;</div>
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span>    }</div>
</div>
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span>    <span class="comment"></span></div>
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno">   72</span><span class="comment">    /// \brief Sets the value of k- the number of steps of the Gibbs Chain </span></div>
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno">   73</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno">   74</span><span class="comment">    /// @param k  the number of steps</span></div>
<div class="foldopen" id="foldopen00075" data-start="{" data-end="}">
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno"><a class="line" href="classshark_1_1_contrastive_divergence.html#ad2f9ab74c8ffca4d383827161fa2df90">   75</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_contrastive_divergence.html#ad2f9ab74c8ffca4d383827161fa2df90" title="Sets the value of k- the number of steps of the Gibbs Chain.">setK</a>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> k){</div>
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno">   76</span>        m_k = k;</div>
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno">   77</span>    }</div>
</div>
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span> </div>
<div class="foldopen" id="foldopen00079" data-start="{" data-end="}">
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno"><a class="line" href="classshark_1_1_contrastive_divergence.html#ac24476a87828b2d74f9da679e8d06b57">   79</a></span>    <a class="code hl_typedef" href="classshark_1_1_abstract_objective_function.html#a59bfea031628e16737c66e7117eba7b5">SearchPointType</a> <a class="code hl_function" href="classshark_1_1_contrastive_divergence.html#ac24476a87828b2d74f9da679e8d06b57" title="Proposes a starting point in the feasible search space of the function.">proposeStartingPoint</a>()<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span>        <span class="keywordflow">return</span>  mpe_rbm-&gt;parameterVector();</div>
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno">   81</span>    }</div>
</div>
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span>    <span class="comment"></span></div>
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span><span class="comment">    /// \brief Returns the number of variables of the RBM.</span></div>
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span><span class="comment">    /// @return the number of variables of the RBM</span></div>
<div class="foldopen" id="foldopen00086" data-start="{" data-end="}">
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno"><a class="line" href="classshark_1_1_contrastive_divergence.html#a30ad9c94feed0c0a74c38e6c3ac81031">   86</a></span><span class="comment"></span>    std::size_t <a class="code hl_function" href="classshark_1_1_contrastive_divergence.html#a30ad9c94feed0c0a74c38e6c3ac81031" title="Returns the number of variables of the RBM.">numberOfVariables</a>()<span class="keyword">const</span>{</div>
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span>        <span class="keywordflow">return</span> mpe_rbm-&gt;numberOfParameters();</div>
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno">   88</span>    }</div>
</div>
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span>    <span class="comment"></span></div>
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span><span class="comment">    /// \brief Returns the number of batches of the dataset that are used in every iteration.</span></div>
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span><span class="comment">    /// If it is less than all batches, the batches are chosen at random. if it is 0, all batches are used</span></div>
<div class="foldopen" id="foldopen00093" data-start="{" data-end="}">
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno"><a class="line" href="classshark_1_1_contrastive_divergence.html#ab29e7de311d214da805621aa15e980c7">   93</a></span><span class="comment"></span>    std::size_t <a class="code hl_function" href="classshark_1_1_contrastive_divergence.html#ab29e7de311d214da805621aa15e980c7" title="Returns the number of batches of the dataset that are used in every iteration.">numBatches</a>()<span class="keyword">const</span>{</div>
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno">   94</span>        <span class="keywordflow">return</span> m_numBatches;</div>
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span>    }</div>
</div>
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span>    <span class="comment"></span></div>
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno">   97</span><span class="comment">    /// \brief Returns a reference to the number of batches of the dataset that are used in every iteration.</span></div>
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno">   98</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno">   99</span><span class="comment">    /// If it is less than all batches, the batches are chosen at random.if it is 0, all batches are used.</span></div>
<div class="foldopen" id="foldopen00100" data-start="{" data-end="}">
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno"><a class="line" href="classshark_1_1_contrastive_divergence.html#a5c0e83eb0df010209838be858670a266">  100</a></span><span class="comment"></span>    std::size_t&amp; <a class="code hl_function" href="classshark_1_1_contrastive_divergence.html#a5c0e83eb0df010209838be858670a266" title="Returns a reference to the number of batches of the dataset that are used in every iteration.">numBatches</a>(){</div>
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span>        <span class="keywordflow">return</span> m_numBatches;</div>
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span>    }</div>
</div>
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span>    </div>
<div class="foldopen" id="foldopen00104" data-start="{" data-end="}">
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno"><a class="line" href="classshark_1_1_contrastive_divergence.html#aee460b5ff7fc5f979bad66fd6d4c0cbc">  104</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_contrastive_divergence.html#aee460b5ff7fc5f979bad66fd6d4c0cbc">setRegularizer</a>(<span class="keywordtype">double</span> factor, <a class="code hl_class" href="classshark_1_1_abstract_objective_function.html">SingleObjectiveFunction</a>* regularizer){</div>
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno">  105</span>        m_regularizer = regularizer;</div>
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno">  106</span>        m_regularizationStrength = factor;</div>
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span>    }</div>
</div>
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span>    <span class="comment"></span></div>
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span><span class="comment">    /// \brief Gives the CD-k approximation of the log-likelihood gradient.</span></div>
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span><span class="comment">    /// @param parameter the actual parameters of the RBM</span></div>
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno">  112</span><span class="comment">    /// @param derivative holds later the CD-k approximation of the log-likelihood gradient</span></div>
<div class="foldopen" id="foldopen00113" data-start="{" data-end="}">
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno"><a class="line" href="classshark_1_1_contrastive_divergence.html#a62d35d6d633fd3688fcfa2fe8926ad0d">  113</a></span><span class="comment"></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_contrastive_divergence.html#a62d35d6d633fd3688fcfa2fe8926ad0d" title="Gives the CD-k approximation of the log-likelihood gradient.">evalDerivative</a>( <a class="code hl_typedef" href="classshark_1_1_abstract_objective_function.html#a59bfea031628e16737c66e7117eba7b5">SearchPointType</a> <span class="keyword">const</span> &amp; parameter, <a class="code hl_typedef" href="classshark_1_1_abstract_objective_function.html#a29804371954a360f09696adea7cfd839">FirstOrderDerivative</a> &amp; derivative )<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span>        mpe_rbm-&gt;setParameterVector(parameter);</div>
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno">  115</span>        derivative.resize(mpe_rbm-&gt;numberOfParameters());</div>
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span>        derivative.clear();</div>
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span>        </div>
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span>        std::size_t batchesForTraining = m_numBatches &gt; 0? m_numBatches: m_data.<a class="code hl_function" href="group__shark__globals.html#gabd82edf467b9b82f4b0a1e70fd695311" title="Returns the number of batches of the set.">numberOfBatches</a>();</div>
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno">  119</span>        std::size_t elements = 0;</div>
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno">  120</span>        <span class="comment">//get the batches for this iteration</span></div>
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span>        std::vector&lt;std::size_t&gt; batchIds(m_data.<a class="code hl_function" href="group__shark__globals.html#gabd82edf467b9b82f4b0a1e70fd695311" title="Returns the number of batches of the set.">numberOfBatches</a>());</div>
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span>        {</div>
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno">  123</span>            <span class="keywordflow">for</span>(std::size_t i = 0; i != m_data.<a class="code hl_function" href="group__shark__globals.html#gabd82edf467b9b82f4b0a1e70fd695311" title="Returns the number of batches of the set.">numberOfBatches</a>(); ++i){</div>
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span>                batchIds[i] = i;</div>
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno">  125</span>            }</div>
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span>            std::shuffle(batchIds.begin(),batchIds.end(),mpe_rbm-&gt;rng());</div>
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno">  127</span>            <span class="keywordflow">for</span>(std::size_t i = 0; i != batchesForTraining; ++i){</div>
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span>                elements += m_data.<a class="code hl_function" href="group__shark__globals.html#ga73034ee5639176b0d45e1059859d0f0a">batch</a>(batchIds[i]).size1();</div>
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span>            }</div>
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno">  130</span>        }</div>
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno">  131</span>        </div>
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno">  132</span>        std::size_t threads = std::min&lt;std::size_t&gt;(batchesForTraining,<a class="code hl_define" href="_open_m_p_8h.html#afe4c8d5e3dea340345071efe7bf69a0a">SHARK_NUM_THREADS</a>);</div>
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span>        std::size_t <a class="code hl_function" href="classshark_1_1_contrastive_divergence.html#ab29e7de311d214da805621aa15e980c7" title="Returns the number of batches of the dataset that are used in every iteration.">numBatches</a> = batchesForTraining/threads;</div>
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno">  134</span>        </div>
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span>        </div>
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno">  136</span>        <a class="code hl_define" href="_open_m_p_8h.html#a8a63d79e2c3625260e6092d933f21a98" title="Set of macros to help usage of OpenMP with Shark.">SHARK_PARALLEL_FOR</a>(<span class="keywordtype">int</span> t = 0; t &lt; (int)threads; ++t){</div>
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span>            <span class="keyword">typename</span> <a class="code hl_typedef" href="classshark_1_1_r_b_m.html#a916086702525de4b9ccd1a715f4317d8" title="Type of the gradient calculator.">RBM::GradientType</a> empiricalAverage(mpe_rbm);</div>
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno">  138</span>            <span class="keyword">typename</span> <a class="code hl_typedef" href="classshark_1_1_r_b_m.html#a916086702525de4b9ccd1a715f4317d8" title="Type of the gradient calculator.">RBM::GradientType</a> modelAverage(mpe_rbm);</div>
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno">  139</span>            </div>
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno">  140</span>            std::size_t threadElements = 0;</div>
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno">  141</span>            </div>
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno">  142</span>            std::size_t batchStart = t*<a class="code hl_function" href="classshark_1_1_contrastive_divergence.html#ab29e7de311d214da805621aa15e980c7" title="Returns the number of batches of the dataset that are used in every iteration.">numBatches</a>;</div>
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno">  143</span>            std::size_t <a class="code hl_function" href="namespaceshark.html#a30ec7154011b330cf99b48a14a973f64">batchEnd</a> = (t== (int)threads-1)? batchesForTraining : batchStart+<a class="code hl_function" href="classshark_1_1_contrastive_divergence.html#ab29e7de311d214da805621aa15e980c7" title="Returns the number of batches of the dataset that are used in every iteration.">numBatches</a>;</div>
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno">  144</span>            <span class="keywordflow">for</span>(std::size_t i = batchStart; i != <a class="code hl_function" href="namespaceshark.html#a30ec7154011b330cf99b48a14a973f64">batchEnd</a>; ++i){</div>
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno">  145</span>                RealMatrix <span class="keyword">const</span>&amp; batch = m_data.<a class="code hl_function" href="group__shark__globals.html#ga73034ee5639176b0d45e1059859d0f0a">batch</a>(batchIds[i]);</div>
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno">  146</span>                threadElements += batch.size1();</div>
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno">  147</span>                </div>
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno">  148</span>                <span class="comment">//create the batches for evaluation</span></div>
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno">  149</span>                <span class="keyword">typename</span> Operator::HiddenSampleBatch hiddenBatch(batch.size1(),mpe_rbm-&gt;numberOfHN());</div>
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno">  150</span>                <span class="keyword">typename</span> Operator::VisibleSampleBatch visibleBatch(batch.size1(),mpe_rbm-&gt;numberOfVN());</div>
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno">  151</span>                </div>
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno">  152</span>                visibleBatch.state = batch;</div>
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno">  153</span>                m_operator.precomputeHidden(hiddenBatch,visibleBatch,blas::repeat(1.0,batch.size1()));</div>
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno">  154</span>                m_operator.sampleHidden(hiddenBatch);</div>
<div class="line"><a id="l00155" name="l00155"></a><span class="lineno">  155</span>                empiricalAverage.addVH(hiddenBatch,visibleBatch);</div>
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno">  156</span>                </div>
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno">  157</span>                <span class="keywordflow">for</span>(std::size_t step = 0; step != m_k; ++step){</div>
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno">  158</span>                    m_operator.precomputeVisible(hiddenBatch, visibleBatch,blas::repeat(1.0,batch.size1()));</div>
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno">  159</span>                    m_operator.sampleVisible(visibleBatch);</div>
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno">  160</span>                    m_operator.precomputeHidden(hiddenBatch, visibleBatch,blas::repeat(1.0,batch.size1()));</div>
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno">  161</span>                    <span class="keywordflow">if</span>( step != m_k-1){</div>
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno">  162</span>                        m_operator.sampleHidden(hiddenBatch);</div>
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno">  163</span>                    }</div>
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno">  164</span>                }</div>
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno">  165</span>                modelAverage.addVH(hiddenBatch,visibleBatch);</div>
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno">  166</span>            }</div>
<div class="line"><a id="l00167" name="l00167"></a><span class="lineno">  167</span>            <a class="code hl_define" href="_open_m_p_8h.html#a6de33df9d72bea69f903cffb391e7121">SHARK_CRITICAL_REGION</a>{</div>
<div class="line"><a id="l00168" name="l00168"></a><span class="lineno">  168</span>                <span class="keywordtype">double</span> weight = threadElements/double(elements);</div>
<div class="line"><a id="l00169" name="l00169"></a><span class="lineno">  169</span>                noalias(derivative) += weight*(modelAverage.result() - empiricalAverage.result());</div>
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno">  170</span>            }</div>
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno">  171</span>            </div>
<div class="line"><a id="l00172" name="l00172"></a><span class="lineno">  172</span>        }</div>
<div class="line"><a id="l00173" name="l00173"></a><span class="lineno">  173</span>        </div>
<div class="line"><a id="l00174" name="l00174"></a><span class="lineno">  174</span>        <span class="keywordflow">if</span>(m_regularizer){</div>
<div class="line"><a id="l00175" name="l00175"></a><span class="lineno">  175</span>            <a class="code hl_typedef" href="classshark_1_1_abstract_objective_function.html#a29804371954a360f09696adea7cfd839">FirstOrderDerivative</a> regularizerDerivative;</div>
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno">  176</span>            m_regularizer-&gt;<a class="code hl_function" href="classshark_1_1_abstract_objective_function.html#a53df2ac5d82c608ea938dc1e3a0c0617" title="Evaluates the objective function and calculates its gradient.">evalDerivative</a>(parameter,regularizerDerivative);</div>
<div class="line"><a id="l00177" name="l00177"></a><span class="lineno">  177</span>            noalias(derivative) += m_regularizationStrength*regularizerDerivative;</div>
<div class="line"><a id="l00178" name="l00178"></a><span class="lineno">  178</span>        }</div>
<div class="line"><a id="l00179" name="l00179"></a><span class="lineno">  179</span>        </div>
<div class="line"><a id="l00180" name="l00180"></a><span class="lineno">  180</span>        <span class="keywordflow">return</span> std::numeric_limits&lt;double&gt;::quiet_NaN();</div>
<div class="line"><a id="l00181" name="l00181"></a><span class="lineno">  181</span>    }</div>
</div>
<div class="line"><a id="l00182" name="l00182"></a><span class="lineno">  182</span> </div>
<div class="line"><a id="l00183" name="l00183"></a><span class="lineno">  183</span><span class="keyword">private</span>:    </div>
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno">  184</span>    <a class="code hl_class" href="classshark_1_1_unlabeled_data.html" title="Data set for unsupervised learning.">UnlabeledData&lt;RealVector&gt;</a> m_data;</div>
<div class="line"><a id="l00185" name="l00185"></a><span class="lineno">  185</span>    <a class="code hl_class" href="classshark_1_1_r_b_m.html" title="stub for the RBM class. at the moment it is just a holder of the parameter set and the Energy.">RBM</a>* mpe_rbm;</div>
<div class="line"><a id="l00186" name="l00186"></a><span class="lineno">  186</span>    Operator m_operator;</div>
<div class="line"><a id="l00187" name="l00187"></a><span class="lineno">  187</span>    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> m_k;</div>
<div class="line"><a id="l00188" name="l00188"></a><span class="lineno">  188</span>    std::size_t m_numBatches;<span class="comment">///&lt; number of batches used in every iteration. 0 means all.</span></div>
<div class="line"><a id="l00189" name="l00189"></a><span class="lineno">  189</span> </div>
<div class="line"><a id="l00190" name="l00190"></a><span class="lineno">  190</span>    <a class="code hl_class" href="classshark_1_1_abstract_objective_function.html">SingleObjectiveFunction</a>* m_regularizer;</div>
<div class="line"><a id="l00191" name="l00191"></a><span class="lineno">  191</span>    <span class="keywordtype">double</span> m_regularizationStrength;</div>
<div class="line"><a id="l00192" name="l00192"></a><span class="lineno">  192</span>};  </div>
</div>
<div class="line"><a id="l00193" name="l00193"></a><span class="lineno">  193</span>    </div>
<div class="line"><a id="l00194" name="l00194"></a><span class="lineno">  194</span>}</div>
<div class="line"><a id="l00195" name="l00195"></a><span class="lineno">  195</span> </div>
<div class="line"><a id="l00196" name="l00196"></a><span class="lineno">  196</span><span class="preprocessor">#endif</span></div>
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